Automated document recognition, identification, and data extraction

ABSTRACT

A method for automated document recognition, identification, and data extraction is described herein. The method comprises receiving, by the processor, an image of a document associated with a user. The image is analyzed using optical character recognition to obtain image data, wherein the image data includes text zones. Based on the image data, the image is compared to one or more document templates. Based on the comparison, a document template having the highest degree of coincidence with the image is determined. The text zones of the image are associated with text zones of the document template to determine a type of data in each text zone. The data is structured into a standard format to obtain structured data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present utility patent application is a continuation of and claimspriority benefit of U.S. patent application Ser. No. 14/468,173, filedon Aug. 25, 2014, and issued on Mar. 31, 2015 as U.S. Pat. No.8,995,774, which is related to and claims priority benefit of the U.S.provisional application No. 61/880,060, filed on Sep. 19, 2013 and U.S.provisional application No. 61/880,062, filed on Sep. 19, 2013 under 35U.S.C. 119(e). The disclosures of the foregoing provisional applicationsare incorporated herein by reference for all purposes to the extent thatsuch subject matter is not inconsistent herewith or limiting hereof.

TECHNICAL FIELD

The present disclosure relates generally to data processing and, morespecifically, to automated document recognition, identification, anddata extraction.

BACKGROUND

Computer technologies can automate many routine processes. Manycomputerized services involve banking, travel, employment, immigration,social networks, and so forth require users to prove their identity.Users may provide their credentials online, but a more thoroughidentification involving an identification document may be required.Traditionally, identification documents have been verified visually by ahuman. Besides being time consuming and prone to errors, visualverification may not be possible in a computerized environment. Thus,speedy and automated verification of identity documents may improveonline transactions.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Provided are methods and systems for automated document recognition,identification, and data extraction. The system for automated documentrecognition, identification, and data extraction may comprise aprocessor and a database communicatively coupled to the processor. Theprocessor may be configured to receive an image of a document associatedwith a user (e.g., a passport, a driver's license, and so forth). Theidentification (ID) image may be processed by optical characterrecognition (OCR) software integrated in the system for automateddocument recognition, identification, and data extraction to retrieveand extract textual data from the image.

Additionally, the ID image may be compared with one or more documenttemplates stored in a database of the system for automated documentrecognition, identification, and data extraction. For example, the IDdocument may be a driver's license, a credit card, and the like. Anappropriate document template may be located based on predeterminedcriteria. The predetermined criteria may include, for example, a degreeof coincidence. The type of the ID document is determined according tothe document template having the highest similarity to the ID document.In one example, information extracted from the image may be utilized todetermine whether one or more criteria have been met for the appropriatedocument template. The document template may be utilized to map the textdata to data groups that may be stored in a standardized format.

A document template may contain information that allows extraction ofindividual data elements from the text file using an OCR process, whichallows associating specific text fragments with data fields according tothe determined type of the document. In this way, the document dataextracted through OCR may be structured into a standard format.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example and not limitation in thefigures of the accompanying drawings, in which like references indicatesimilar elements.

FIG. 1 illustrates an environment within which systems and methods forautomated document recognition, identification, and data extraction canbe implemented.

FIG. 2 is a block diagram showing a system for automated documentrecognition, identification, and data extraction.

FIG. 3 is a process flow diagram showing a method for automated documentrecognition, identification, and data extraction.

FIG. 4 illustrates example recognizing of a document.

FIG. 5 illustrates example focusing on an image of the document.

FIG. 6 illustrates example comparing of an identification document todocument templates.

FIG. 7 shows a diagrammatic representation of a computing device for amachine in the exemplary electronic form of a computer system, withinwhich a set of instructions for causing the machine to perform any oneor more of the methodologies discussed herein can be executed.

DETAILED DESCRIPTION

The following detailed description includes references to theaccompanying drawings, which form a part of the detailed description.The drawings show illustrations in accordance with exemplaryembodiments. These exemplary embodiments, which are also referred toherein as “examples,” are described in enough detail to enable thoseskilled in the art to practice the present subject matter. Theembodiments can be combined, other embodiments can be utilized, orstructural, logical, and electrical changes can be made withoutdeparting from the scope of what is claimed. The following detaileddescription is therefore not to be taken in a limiting sense, and thescope is defined by the appended claims and their equivalents. In thisdocument, the terms “a” and “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a nonexclusive “or,” such that “A or B”includes “A but not B,” “B but not A,” and “A and B,” unless otherwiseindicated.

Techniques of the embodiments disclosed herein may be implemented usinga variety of technologies. For example, the methods described herein maybe implemented in software executing on a computer system or in hardwareutilizing either a combination of microprocessors or other speciallydesigned application-specific integrated circuits (ASICs), programmablelogic devices, or various combinations thereof. In particular, themethods described herein may be implemented by a series ofcomputer-executable instructions residing on a storage medium such as adisk drive or computer-readable medium. It should be noted that methodsdisclosed herein can be implemented by a computer (e.g., a server,desktop computer, tablet computer, laptop computer), game console,handheld gaming device, cellular phone, smart phone, smart televisionsystem, and so forth.

Users may be asked to prove identity and provide ID information invarious circumstances. Since computers are now incorporated into almostall spheres of our life, the identification information is oftenprovided in a digital form for various purposes, for example, in airtravel check-in, hotel registration, social network login, onlineapplications submission, and so forth.

To facilitate the identification process, a user may provide an IDdocument associated with himself or another user. For this purpose, theuser may capture an image of the ID document by using a regular scanner,a photo camera, a smart phone, or the like. An image or a scanassociated with the ID document may be uploaded to the system forautomated document recognition, identification, and data extractioneither via a mobile application, a stand-alone web application, or via afully integrated service (XML, i-frame).

In some embodiments, the image may be captured by a camera associatedwith a user (e.g., a phone camera, a tablet personal computer (PC)camera, and so forth). To increase the quality of the image taken by thecamera, the user may be prompted to hold the ID document in front of thecamera. The video stream from the camera may be transmitted to a systemfor automated document recognition, identification, and data extraction.The system for automated document recognition, identification, and dataextraction may analyze the video stream to detect a shape that can beassociated with the ID document. Since in most cases ID documents have arectangular shape, the detection may involve determining four angles ofabout 90 degrees each, which form a rectangular shape. When such arectangular shape is determined in the video stream, the ID document maybe identified.

Then, the system for automated document recognition, identification, anddata extraction may automatically improve the video stream to improverepresentation of the identified ID document. For example, the systemfor automated document recognizing and image taking may focus on the IDdocument, sharpen the image, remove blurring, adjust brightness andcolors, and so forth. In such a way, the quality of the image may beimproved, white spots removed, and the like. This may improverecognition of text in the ID document and thus facilitate further dataextraction and structuring. A still image of the improved representationof the ID document may be automatically extracted from the video streamand received by the system for automated document recognition,identification, and data extraction.

The system for automated document recognition, identification, and dataextraction may analyze the extracted still image and provide variousdata associated with the ID document (for example, issue date, holder'sname, holder's age, and so forth). The system for automated documentrecognition, identification, and data extraction may automaticallyimprove the image of the recognized ID document by removing blurring,adjusting brightness and colors, and so forth. In such a way, thequality of the image may be improved. Additionally, authenticitycharacteristics of the identification document may be ascertained. Basedon the authentication, results of a biometric verification may beprovided.

The system for automated document recognition, identification, and dataextraction may use information retrieved from the ID document topopulate various online forms. Thus, instead of manually typing personalinformation that can be normally found in an ID document, the user mayjust snap a picture of his ID document and send it to the system forautomated document recognition, identification, and data extraction. Thesystem will analyze the image, parse the data in the image, and send theparsed data from the ID document back to the user for automaticpopulating of the fields of the online form. The information may includea first name, last name, address, document number, document expirationdata, user gender, age, state, city, zip code, vehicle class, and thelike.

The user may be prompted to take a picture of the ID document using acamera and send the image to the system for automated documentrecognition, identification, and data extraction. The data included inthe image of the ID document may be extracted and one or more fields ina form populated with at least a portion of the extracted data. Once theform is completed it may be transmitted to a customer in real time.

In certain example embodiments, the system for automated documentrecognition, identification, and data extraction may be furtherconfigured to process other types of documents including e-mails, faxes,and various electronic files.

FIG. 1 illustrates an environment 100 within which the systems andmethods for automated document recognition, identification, and dataextraction can be implemented, in accordance with some embodiments. Asystem 200 for automated document recognition, identification, and dataextraction may provide document recognition capabilities via an onlineportal, special-purpose application, cloud-based application,server-based distributed application, and so forth. When the system 200is a server-based distributed application, it may include a centralcomponent residing on a server and one or more client applicationsresiding on client devices and communicating with the central componentvia a network 110.

The network 110 may include the Internet or any other network capable ofcommunicating data between devices. Suitable networks may include orinterface with any one or more of, for instance, a local intranet, a PAN(Personal Area Network), a LAN (Local Area Network), a WAN (Wide AreaNetwork), a MAN (Metropolitan Area Network), a virtual private network(VPN), a storage area network (SAN), a frame relay connection, anAdvanced Intelligent Network (AIN) connection, a synchronous opticalnetwork (SONET) connection, a digital T1, T3, E1 or E3 line, DigitalData Service (DDS) connection, DSL (Digital Subscriber Line) connection,an Ethernet connection, an ISDN (Integrated Services Digital Network)line, a dial-up port such as a V.90, V.34 or V.34bis analog modemconnection, a cable modem, an ATM (Asynchronous Transfer Mode)connection, or an FDDI (Fiber Distributed Data Interface) or CDDI(Copper Distributed Data Interface) connection. Furthermore,communications may also include links to any of a variety of wirelessnetworks, including WAP (Wireless Application Protocol), GPRS (GeneralPacket Radio Service), GSM (Global System for Mobile Communication),CDMA (Code Division Multiple Access) or TDMA (Time Division MultipleAccess), cellular phone networks, GPS (Global Positioning System), CDPD(cellular digital packet data), RIM (Research in Motion, Limited) duplexpaging network, Bluetooth radio, or an IEEE 802.11-based radio frequencynetwork. The network 110 can further include or interface with any oneor more of an RS-232 serial connection, an IEEE-1394 (Firewire)connection, a Fiber Channel connection, an IrDA (infrared) port, a SCSI(Small Computer Systems Interface) connection, a USB (Universal SerialBus) connection or other wired or wireless, digital or analog interfaceor connection, mesh or Digi® networking. The network 110 may be anetwork of data processing nodes that are interconnected for the purposeof data communication.

A user 140 may communicate with the system 200 via a client applicationavailable through a client device 130. In still other embodiments, thesystem 200 may be a cloud-based application with the central componentresiding on a server and accessible via a web browser on the clientdevice 130.

To verify his identity, the user 140 may send an image 145 associatedwith an ID document 120 to the system 200 for automated documentrecognition, identification, and data extraction via a network (e.g.,the Internet). For this purpose, the user may utilize, for example, aweb application or a mobile application installed on the client device130. The image may show the ID document 120. The image 145 of the IDdocument 120 may be taken by a camera associated with the user 140. Thecamera may, for example, include a phone camera, a notebook camera, a PCcamera, and so forth.

Alternatively, the user 140 may capture the image 145 of the ID document120 by using a regular scanner, a photo camera, a smart phone, or thelike. An image or a scan associated with the ID document 120 may beuploaded to the system 200 for automated document recognition,identification, and data extraction either via a mobile application, astand-alone web application, or via a fully integrated service (XML,i-frame).

In some embodiments, the image may be captured by a camera associatedwith the user 140 (e.g. a phone camera, a tablet PC camera, and soforth). The system 200 for automated document recognition,identification, and data extraction may assist the user 140 in imagetaking. The system 200 for automated document recognition,identification, and data extraction may provide instructions to the user140 via a screen or dynamic of the client device 130. Following theinstructions, the user 140 may provide the ID document 120 (associatedwith the user 140) to be captured by a camera (e.g., web camera) of theclient device 130. The ID document 120 may include a driver's license, agovernment issued ID, a passport, a student ID, and the like.

The captured video stream including the document image may be processedby the system 200 for automated document recognition, identification anddata extraction. In many embodiments, processing will be performed inthe client device 130 that captures the image of the ID document 120.Alternatively, processing may be performed remotely on a server. Forexample, the video stream of the ID document 120 may be provided to thecentral component of the system 200 via the network 110. The system 200for automated document recognition, identification, and data extractionmay recognize the ID document 120 in the video stream by identifyingfour angles about 90 degrees each. The four angles may formsubstantially a rectangular shape. Based on the recognizing, the system200 for automated document recognition, identification, and dataextraction may focus on the recognized ID document 120 in the videostream and improve quality of the image by, for example, sharpening theimage, removing blurring, and so forth. Then, a still image of the IDdocument 120 may be extracted from the video stream and transmitted fordata extraction to a server as the image 145. Additionally, the image145 may be stored in a database 150.

The image 145 may be received by a remote server of the system 200 forautomated document recognition, identification, and data extraction.

The system 200 for automated document recognition, identification, anddata extraction may analyze the image 145 and provide various dataassociated with the identification document (for example, issue date,holder's name, holder's age, and so forth). The system 200 for automateddocument recognition, identification, and data extraction mayautomatically improve the image in the recognized ID document byremoving blurring, adjusting brightness and colors, and so forth. Insuch a way, the quality of the image may be improved. Additionally,authenticity characteristics of the ID document 120 may be ascertained.

In some embodiments, the image 145 of the ID document 120 may becorrelated to a plurality of ID templates stored in the database 150 todetermine a type of the ID document 120. For example, it may bedetermined that the image 145 depicts a California driver's license. OCRcan be used to extract data from the image 145. The extracted data canbe associated with fields of the document based on known locations ofsuch fields for this document type. Thus, identification information canbe extracted from the document image 145 and associated with specificfields according to the determined document type. In such a way, theidentification information can be structured. The structured informationcan be transmitted to the client device 130 or to another device,location, or resource and automatically inserted in appropriate fieldsof an online form.

FIG. 2 shows a detailed block diagram of the system 200 for automateddocument recognition, identification and data extraction, in accordancewith an example embodiment. The system 200 may include a processor 210,a database 220, and, optionally, a graphical output interface 230. Theprocessor 210 may include a programmable processor, such as amicrocontroller, central processing unit (CPU), and so forth. In otherembodiments, the processor 210 may include an ASIC or programmable logicarray (PLA), such as a field programmable gate array (FPGA), designed toimplement the functions performed by the system 200.

The processor 210 may be configured to receive an image associated withan ID document associated with a user. The image may be received by theprocessor 210 and stored in the database 220. The processor 210 mayanalyze the image using OCR to detect various zones on the imagecontaining information associated with the ID document and its holder.To determine the type of the document provided, the processor 210 may beconfigured to match the provided image with one or more documenttemplates stored in the database 220. After finding the template havingthe highest degree of coincidence with the provided image, the processor210 may be configured to determine the type of information contained ineach zone of the image. The processor 210 may be further configured tostructure the retrieved information and upload the information to one ormore fields of a fill-out form. In some embodiments, the completedfill-out form is made accessible on system 200.

Additionally, the processor 210 may be configured to receive a videostream associated with the ID document, recognize the document by ashape, improve an image of the document, and extract a still image ofthe ID document from the video stream. The still image may be stored inthe database 220.

Furthermore, the image of the ID document may be analyzed to checkauthenticity characteristics of the identification document. Theprocessor 210 may provide results of the verification based on theanalysis. The processor 210 may comprise or may be in communication withmedia, for example, computer-readable media, which stores instructionsthat, when executed by the processor, cause the processor to perform theelements described herein. Additionally, the processor 210 may operateany operating system capable of supporting locally executedapplications, client-server based applications, and/or browser orbrowser-enabled applications.

In some embodiments, the system 200 for automated document recognition,identification, and data extraction may be a part of a biometricverification system. Thus, the system 200 for automated documentrecognition, identification, and data extraction may recognize the IDdocument and extract the image of the document, which image may befurther used for biometric verification of the user.

FIG. 3 is a process flow diagram showing a method 300 for automateddocument recognition, identification, and data extraction within theenvironment described with reference to FIG. 1. The method 300 may beperformed by a logic that may include hardware (e.g., dedicated logic,programmable logic, and microcode), software (such as software run on ageneral-purpose computer system or a dedicated machine), or acombination of both. In one exemplary embodiment, the processing logicmay reside at a CPU, which may be a programmable module that may manageand connect to other modules in the system as well as implement theinstructions according to a set of rules in the form of a program code.Such program code may reside in a memory that also stores examplerecording sound data of various types and characteristics. Althoughvarious elements may be configured to perform some or all of the variousoperations described herein, fewer or more elements may be provided andstill fall within the scope of various embodiments.

As shown in FIG. 3, the method 300 may commence with receiving a videostream associated with a document from a client device at optionaloperation 302. The document may include a government issued ID, astudent ID, an employment ID, a driver's license, a passport, a traveldocument, and so forth. The received video stream may be captured by acamera associated with the user (for example, a standalone camera, or acamera associated with a user device, such as a smart phone, a PC, atablet PC, and so forth).

At optional operation 304, the document may be recognized in the videostream received by determining at least four angles. The angles may besubstantially 90 degrees each and form a rectangular shape. The receivedvideo may be adjusted to focus on the recognized document and otherwiseimprove an image of the document at optional operation 306. Then, astill image of the document may be extracted from the video stream atoptional operation 308.

The extracted image of the document may be received by a server of thesystem 200 for automated document recognition, identification, and dataextraction at operation 310. The received image may include a picture, ascan, and so forth. The image may be captured by a camera associatedwith the user (for example, a standalone camera, a camera of a userdevice, such as a smart phone, a PC, a tablet PC, and so forth, scannedby a scanner, or obtained otherwise).

The method 300 may proceed with analyzing, by the processor, the imageusing OCR to obtain image data at operation 312. The image data maycontain text zones with a specific text therein relating to dataassociated with the ID document. To identify the type of documentprovided, the image may be compared to one or more document templatespre-stored in the database on the server at operation 314. To performthis comparison, coordinates of text zones on the image of the IDdocument can be matched to coordinates of text zones of availabledocument templates. In some embodiments, only a partial comparison canbe performed. In some cases, the results may state that documentidentification cannot be performed.

At operation 316, if the comparison is successful, a document templatehaving the highest similarity with the image may be determined.

The method 300 may further proceed with associating the text fields ofthe image with text fields of the best-matching document template todetermine the type of data in each text field at operation 318. Forexample, it may be determined that the text field in a right uppercorner contains a first name of the ID document holder, the text fieldin the right bottom corner contains a document number, and so forth.Based on the associating, the data may be structured according to astandard format at operation 320. The structured data may be furtherused to populate the field of a fill-out form.

FIG. 4 illustrates example recognizing 400 of a document 410 in a videostream. The video stream may be analyzed to detect a shape associatedwith the document. Since many documents have a rectangular shape, thedocument 410 may be recognized by its corners forming a rectangular. Forexample, the document 410 may be identified by four angles 420 of 90degrees each. The four angles 420 form a substantially rectangularshape.

When the document 410 is recognized, an image associated with thedocument 410 may be improved in various ways. Improving an image isillustrated by FIG. 5.

FIG. 5 shows an example of improving quality 500 of an image of thedocument 410 in the video stream, in accordance with an exampleembodiment. The image improving may include focusing (as shown by focus510) on the identification document 410, adjusting colors, adjustingbrightness, removing blurring, and so forth. In this manner, furtherdata extracting from the document may be facilitated. For example, thesystem for automated document recognition, identification, and dataextraction may use OCR to extract data shown in the document. Byimproving the image quality, the system may eliminate errors possiblewith OCR, filter out visual noise, and so forth.

The system for automated document recognition, identification, and dataextraction may extract the improved image of the document 410 from thevideo stream and store it to the database.

In some embodiments, the system for automated document recognition,identification, and data extraction may further determine type of thedocument in the stored image and extract the document data.

To determine the document type, the layout of the document 410 may becompared to layouts of document templates stored in the database. Thecomparison may be based on location of data fields, photos, holograms,and so forth. Additionally, fonts used in the document, countryindication, and other specifics may be applied as filters to narrow downthe number of templates that are matches to the document. The templatesmay provide reference data, such as location of specific information.Reference data facilitates extracting information from the document 410and helps in structuring the extracted information.

In some embodiments, the data extracted from the document 410 is used tofill in web forms or blanks where personal information of the documentholder should be specified.

FIG. 6 illustrates comparing 600 an ID document 610 to a number ofdocument templates 620, 630, 640, 650, in accordance with someembodiments. A user may take a picture of ID document 610 and upload adigital image of the ID document 610 to the system. The image receivedby the system for automated document recognition, identification, anddata extraction may be subjected to OCR. The ID document 610 may includea number of information fields containing different types of dataassociated with the ID document and its holder, such as first name, lastname, and the like. The information fields may be organized according toone of a plurality of known formats presented by document templatesstored in a database of the system for automated document recognition,identification, and data extraction. The image may be segmented into anumber of zones 612 each relating to a separate field. The distancebetween the zones and the borders of each zone may be defined bycoordinates (x, y) 614, 616. These coordinates 614, 616 may be used tocompare the image to one or more document templates. The documenttemplates can be segmented into a number of zones with a predeterminedset of coordinates and distances between the zones. The coordinates ofthe image and the document templates can be matched and the bestmatching document template can be determined. When the document templateis determined, the zones of the image can be associated with the zonesof the template to determine the types of data contained in each zone ofthe image. The data may then be structured according to the identifiedformat.

FIG. 7 shows a diagrammatic representation of a computing device for amachine in the exemplary electronic form of a computer system 700,within which a set of instructions for causing the machine to performany one or more of the methodologies discussed herein can be executed.In various exemplary embodiments, the machine operates as a standalonedevice or can be connected (e.g., networked) to other machines. In anetworked deployment, the machine can operate in the capacity of aserver or a client machine in a server-client network environment, or asa peer machine in a peer-to-peer (or distributed) network environment.The machine can be a PC, a tablet PC, a set-top box (STB), a cellulartelephone, a digital camera, a portable music player (e.g., a portablehard drive audio device, such as an Moving Picture Experts Group AudioLayer 3 (MP3) player), a web appliance, a network router, a switch, abridge, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

The example computer system 700 includes a processor or multipleprocessors 702, a hard disk drive 704, a main memory 706, and a staticmemory 708, which communicate with each other via a bus 710. Thecomputer system 700 may also include a network interface device 712. Thehard disk drive 704 may include a computer-readable medium 720, whichstores one or more sets of instructions 722 embodying or utilized by anyone or more of the methodologies or functions described herein. Theinstructions 722 can also reside, completely or at least partially,within the main memory 706 and/or within the processors 702 duringexecution thereof by the computer system 700. The main memory 706 andthe processors 702 also constitute machine-readable media.

While the computer-readable medium 720 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“computer-readable medium” shall also be taken to include any mediumthat is capable of storing, encoding, or carrying a set of instructionsfor execution by the machine and that causes the machine to perform anyone or more of the methodologies of the present application, or that iscapable of storing, encoding, or carrying data structures utilized by orassociated with such a set of instructions. The term “computer-readablemedium” shall accordingly be taken to include, but not be limited to,solid-state memories, optical and magnetic media. Such media can alsoinclude, without limitation, hard disks, floppy disks, NAND or NOR flashmemory, digital video disks, RAM, ROM, and the like.

The exemplary embodiments described herein can be implemented in anoperating environment comprising computer-executable instructions (e.g.,software) installed on a computer, in hardware, or in a combination ofsoftware and hardware. The computer-executable instructions can bewritten in a computer programming language or can be embodied infirmware logic. If written in a programming language conforming to arecognized standard, such instructions can be executed on a variety ofhardware platforms and for interfaces to a variety of operating systems.Although not limited thereto, computer software programs forimplementing the present method can be written in any number of suitableprogramming languages such as, for example, C, C++, C# or othercompilers, assemblers, interpreters or other computer languages orplatforms.

Thus, computer-implemented methods and systems for automated documentrecognition, identification, and data extraction are described. Althoughembodiments have been described with reference to specific exemplaryembodiments, it will be evident that various modifications and changescan be made to these exemplary embodiments without departing from thebroader spirit and scope of the present application. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

What is claimed is:
 1. A system comprising: a processor; and a memorycommunicatively coupled to the processor, the memory storinginstructions executable to perform a method, the method including:receiving an image of an identity document, the image being producedusing a video stream; recognizing a plurality of text elements in theimage using optical character recognition; finding a document templateof a plurality of templates having a high degree of coincidence with theimage using a substantially rectangular shape of the image overall, atleast one of the text elements, and a respective location in the imagefor the at least one text element; associating each of the text elementswith a respective field of the document template using the text elementsand a respective location in the image for each of the text elements;placing at least one of the associated text elements in a respectivefield of a form, the respective field of the form corresponding to therespective associated field of the document template; making thecompleted form accessible on the system; placing at least one of theassociated text elements in a respective field of a second form, therespective field of the second form corresponding to the respectiveassociated field of the document template; and making the secondcompleted form accessible on the system.
 2. The system of claim 1wherein the method further includes: ascertaining an authenticitycharacteristic in the image; and providing the authenticitycharacteristic for biometric verification.
 3. The system of claim 1wherein the method further includes: storing the completed form in adata store.
 4. The system of claim 1 wherein the identity document is atleast one of: a government issued identity document, a student identitydocument, an employment identity document, a driver's license, apassport, and a travel document.
 5. The system of claim 1 wherein themethod further includes: improving the received image by removingblurring, adjusting brightness, and adjusting colors.
 6. The system ofclaim 1 wherein the plurality of text elements includes at least one of:a first name, last name, address, document number, document expirationdate, gender, age, state, city, zip code, and vehicle class.
 7. Thesystem of claim 1 wherein each of the plurality of templates isassociated with at least one of: a government issued identity document,a student identity document, an employment identity document, a driver'slicense, a passport, and a travel document.
 8. The system of claim 1wherein the respective location in the image for each of the textelements includes Cartesian coordinates where the origin lies at acorner of the image.
 9. The system of claim 8 wherein the respectivelocation in the image for each of the text elements further includes adistance from another location in the image for another text element anda distance from an edge of the image, the distance determined usingrespective Cartesian coordinates of each of the text elements, theanother text element, and the edge of the image.
 10. Acomputer-implemented method comprising: receiving an image of anidentity document, the image being produced using a video stream;recognizing a plurality of text in the image using optical characterrecognition; finding a document template of a plurality of templateshaving a high degree of coincidence with the image using a substantiallyrectangular shape of the image overall, at least one of the textelements, and a respective location in the image for the at least onetext element; associating each of the text elements with a respectivefield of the document template using the text elements and a respectivelocation in the image for each of the text elements; placing at leastone of the associated text elements in a respective field of a form, therespective field of the form corresponding to the respective associatedfield of the document template; making the completed form accessible ona system; placing at least one of the associated text elements in arespective field of a second form, the respective field of the secondform corresponding to the respective associated field of the documenttemplate; and making the second completed form accessible on a system.11. The method of claim 10 further comprising: ascertaining anauthenticity characteristic in the image; and providing the authenticitycharacteristic for biometric verification.
 12. The method of claim 10further comprising: storing the completed form in a data store.
 13. Themethod of claim 10 wherein the identity document is at least one of: agovernment issued identity document, a student identity document, anemployment identity document, a driver's license, a passport, and atravel document.
 14. The method of claim 10 further comprising:improving the received image by removing blurring, adjusting brightness,and adjusting colors.
 15. The method of claim 10 wherein the pluralityof text elements includes at least one of: a first name, last name,address, document number, document expiration date, gender, age, state,city, zip code, and vehicle class.
 16. The method of claim 10 whereineach of the plurality of templates is associated with at least one of: agovernment issued identity document, a student identity document, anemployment identity document, a driver's license, a passport, and atravel document.
 17. The method of claim 10 wherein the respectivelocation in the image for each of the text elements includes Cartesiancoordinates where the origin lies at a corner of the image.
 18. Themethod of claim 17 wherein the respective location in the image for eachof the text elements further includes a distance from another locationin the image for another text element and a distance from an edge of theimage, the distance determined using respective Cartesian coordinates ofeach of the text elements, the another text element, and the edge of theimage.
 19. A non-transitory computer-readable medium having a programembodied thereon, the program executable by a processor to perform amethod, the method comprising: receiving an image of an identitydocument, the image being produced using a video stream; recognizing aplurality of text in the image using optical character recognition;finding a document template of a plurality of templates having a highdegree of coincidence with the image using a substantially rectangularshape of the image overall, at least one of the text elements, and arespective location in the image for the at least one text element;associating each of the text elements with a respective field of thedocument template using the text elements and a respective location inthe image for each of the text elements; placing at least one of theassociated text elements in a respective field of a form, the respectivefield of the form corresponding to the respective associated field ofthe document template; making the completed form accessible on a system;placing at least one of the associated text elements in a respectivefield of a second form, the respective field of the second formcorresponding to the respective associated field of the documenttemplate; and making the second completed form accessible on a system.20. A system comprising: a processor; and a memory communicativelycoupled to the processor, the memory storing instructions executable toperform a method, the method including: receiving an image of anidentity document, the image being produced using a video stream;improving the image, wherein the improving includes removing blurring,adjusting brightness, and adjusting colors; recognizing a plurality oftext elements in the image using optical character recognition; findinga document template of a plurality of templates having a high degree ofcoincidence with the image using a substantially rectangular shape ofthe image overall, at least one of the text elements, and a respectivelocation in the image for the at least one text element; associatingeach of the text elements with a respective field of the documenttemplate using the text elements and a respective location in the imagefor each of the text elements; placing at least one of the associatedtext elements in a respective field of a form, the respective field ofthe form corresponding to the respective associated field of thedocument template; and making the completed form accessible on thesystem.
 21. The system of claim 20 wherein the method further includes:ascertaining an authenticity characteristic in the image; and providingthe authenticity characteristic for biometric verification.
 22. Thesystem of claim 20 wherein the method further includes: storing thecompleted form in a data store.
 23. The system of claim 20 wherein themethod further includes: placing at least one of the associated textelements in a respective field of a second form, the respective field ofthe second form corresponding to the respective associated field of thedocument template; and making the second completed form accessible onthe system.
 24. The system of claim 20 wherein the identity document isat least one of: a government issued identity document, a studentidentity document, an employment identity document, a driver's license,a passport, and a travel document.
 25. The system of claim 20 whereinthe plurality of text elements includes at least one of: a first name,last name, address, document number, document expiration date, gender,age, state, city, zip code, and vehicle class.
 26. The system of claim20 wherein each of the plurality of templates is associated with atleast one of: a government issued identity document, a student identitydocument, an employment identity document, a driver's license, apassport, and a travel document.
 27. The system of claim 20 wherein therespective location in the image for each of the text elements includesCartesian coordinates where the origin lies at a corner of the image.28. The system of claim 27 wherein the respective location in the imagefor each of the text elements further includes a distance from anotherlocation in the image for another text element and a distance from anedge of the image, the distance determined using respective Cartesiancoordinates of each of the text elements, the another text element, andthe edge of the image.
 29. A system comprising: a processor; and amemory communicatively coupled to the processor, the memory storinginstructions executable to perform a method, the method including:receiving an image of an identity document, the image being producedusing a video stream; recognizing a plurality of text elements in theimage using optical character recognition; finding a document templateof a plurality of templates having a high degree of coincidence with theimage using a substantially rectangular shape of the image overall, atleast one of the text elements, and a respective location in the imagefor the at least one text element, wherein the respective location inthe image for each of the text elements includes Cartesian coordinateswhere the origin lies at a corner of the image and a distance fromanother location in the image for another text element and a distancefrom an edge of the image, the distance determined using respectiveCartesian coordinates of each of the text elements, the another textelement, and the edge of the image; associating each of the textelements with a respective field of the document template using the textelements and a respective location in the image for each of the textelements; placing at least one of the associated text elements in arespective field of a form, the respective field of the formcorresponding to the respective associated field of the documenttemplate; and making the completed form accessible on the system. 30.The system of claim 29 wherein the method further includes: ascertainingan authenticity characteristic in the image; and providing theauthenticity characteristic for biometric verification.
 31. The systemof claim 29 wherein the method further includes: storing the completedform in a data store.
 32. The system of claim 29 wherein the methodfurther includes: placing at least one of the associated text elementsin a respective field of a second form, the respective field of thesecond form corresponding to the respective associated field of thedocument template; and making the second completed form accessible onthe system.
 33. The system of claim 29 wherein the identity document isat least one of: a government issued identity document, a studentidentity document, an employment identity document, a driver's license,a passport, and a travel document.
 34. The system of claim 29 whereinthe method further includes: improving the received image by removingblurring, adjusting brightness, and adjusting colors.
 35. The system ofclaim 29 wherein the plurality of text elements includes at least oneof: a first name, last name, address, document number, documentexpiration date, gender, age, state, city, zip code, and vehicle class.36. The system of claim 29 wherein each of the plurality of templates isassociated with at least one of: a government issued identity document,a student identity document, an employment identity document, a driver'slicense, a passport, and a travel document.
 37. A computer-implementedmethod comprising: receiving an image of an identity document, the imagebeing produced using a video stream; improving the image, wherein theimproving includes removing blurring, adjusting brightness, andadjusting colors; recognizing a plurality of text elements in the imageusing optical character recognition; finding a document template of aplurality of templates having a high degree of coincidence with theimage using a substantially rectangular shape of the image overall, atleast one of the text elements, and a respective location in the imagefor the at least one text element; associating each of the text elementswith a respective field of the document template using the text elementsand a respective location in the image for each of the text elements;placing at least one of the associated text elements in a respectivefield of a form, the respective field of the form corresponding to therespective associated field of the document template; and making thecompleted form accessible on a system.
 38. The method of claim 37further comprising: ascertaining an authenticity characteristic in theimage; and providing the authenticity characteristic for biometricverification.
 39. The method of claim 37 further comprising: storing thecompleted form in a data store.
 40. The method of claim 37 furthercomprising: placing at least one of the associated text elements in arespective field of a second form, the respective field of the secondform corresponding to the respective associated field of the documenttemplate; and making the second completed form accessible on a system.41. The method of claim 37 wherein the identity document is at least oneof: a government issued identity document, a student identity document,an employment identity document, a driver's license, a passport, and atravel document.
 42. The method of claim 37 wherein the plurality oftext elements includes at least one of: a first name, last name,address, document number, document expiration date, gender, age, state,city, zip code, and vehicle class.
 43. The method of claim 37 whereineach of the plurality of templates is associated with at least one of: agovernment issued identity document, a student identity document, anemployment identity document, a driver's license, a passport, and atravel document.
 44. The method of claim 37 wherein the respectivelocation in the image for each of the text elements includes Cartesiancoordinates where the origin lies at a corner of the image.
 45. Themethod of claim 44 wherein the respective location in the image for eachof the text elements further includes a distance from another locationin the image for another text element and a distance from an edge of theimage, the distance determined using respective Cartesian coordinates ofeach of the text elements, the another text element, and the edge of theimage.
 46. A computer-implemented method comprising: receiving an imageof an identity document, the image being produced using a video stream;recognizing a plurality of text elements in the image using opticalcharacter recognition; finding a document template of a plurality oftemplates having a high degree of coincidence with the image using asubstantially rectangular shape of the image overall, at least one ofthe text elements, and a respective location in the image for the atleast one text element, wherein the respective location in the image foreach of the text elements includes Cartesian coordinates where theorigin lies at a corner of the image and a distance from anotherlocation in the image for another text element and a distance from anedge of the image, the distance determined using respective Cartesiancoordinates of each of the text elements, the another text element, andthe edge of the image; associating each of the text elements with arespective field of the document template using the text elements and arespective location in the image for each of the text elements; placingat least one of the associated text elements in a respective field of aform, the respective field of the form corresponding to the respectiveassociated field of the document template; and making the completed formaccessible on a system.
 47. The method of claim 46 further comprising:ascertaining an authenticity characteristic in the image; and providingthe authenticity characteristic for biometric verification.
 48. Themethod of claim 46 further comprising: storing the completed form in adata store.
 49. The method of claim 46 further comprising: placing atleast one of the associated text elements in a respective field of asecond form, the respective field of the second form corresponding tothe respective associated field of the document template; and making thesecond completed form accessible on a system.
 50. The method of claim 46wherein the identity document is at least one of: a government issuedidentity document, a student identity document, an employment identitydocument, a driver's license, a passport, and a travel document.
 51. Themethod of claim 46 further comprising: improving the received image byremoving blurring, adjusting brightness, and adjusting colors.
 52. Themethod of claim 46 wherein the plurality of text elements includes atleast one of: a first name, last name, address, document number,document expiration date, gender, age, state, city, zip code, andvehicle class.
 53. The method of claim 46 wherein each of the pluralityof templates is associated with at least one of: a government issuedidentity document, a student identity document, an employment identitydocument, a driver's license, a passport, and a travel document.